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@ARTICLE{Gorjo:872589,
      author       = {Gorjão, Leonardo and Meirinhos, Francisco},
      title        = {kramersmoyal: {K}ramers--{M}oyal coefficients for
                      stochastic processes},
      journal      = {The journal of open source software},
      volume       = {4},
      number       = {44},
      issn         = {2475-9066},
      reportid     = {FZJ-2020-00087},
      pages        = {1693 -},
      year         = {2019},
      abstract     = {kramersmoyal is a python library to extract the
                      Kramers--Moyal coefficients from timeseries of any dimension
                      and to any desired order. This package employs a
                      non-parametric Nadaraya--Watson estimator, i.e.,
                      kernel-density estimators, to retrieve the drift, diffusion,
                      and higher-order moments of stochastic timeseries of any
                      dimension.},
      cin          = {IEK-STE},
      ddc          = {004},
      cid          = {I:(DE-Juel1)IEK-STE-20101013},
      pnm          = {153 - Assessment of Energy Systems – Addressing Issues of
                      Energy Efficiency and Energy Security (POF3-153) / ES2050 -
                      Energie Sytem 2050 (ES2050) / VH-NG-1025 - Helmholtz Young
                      Investigators Group "Efficiency, Emergence and Economics of
                      future supply networks" $(VH-NG-1025_20112014)$},
      pid          = {G:(DE-HGF)POF3-153 / G:(DE-HGF)ES2050 /
                      $G:(HGF)VH-NG-1025_20112014$},
      typ          = {PUB:(DE-HGF)16},
      doi          = {10.21105/joss.01693},
      url          = {https://juser.fz-juelich.de/record/872589},
}